Computer Vision Engineer

Key Responsibilities

• Design, train, and evaluate computer vision models for detection, segmentation, and classification tasks
• Build and manage data pipelines for image and video preprocessing and annotation
• Optimize model performance for latency, accuracy, and resource constraints
• Deploy models to production using containerized services or edge devices
• Stay current with state-of-the-art research and evaluate new architectures for applicability
• Collaborate with product teams to define requirements and success metrics for vision features
• Build monitoring and evaluation systems to track model performance in production

Required Skills & Experience

• 3+ years of experience in computer vision or related ML fields
• Strong proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
• Experience with CNN architectures (ResNet, EfficientNet, YOLO, Vision Transformers)
• Hands-on experience with image processing libraries (OpenCV, PIL/Pillow)
• Familiarity with model training workflows: data augmentation, transfer learning, hyperparameter tuning
• Experience deploying ML models to production (ONNX, TensorRT, or TorchServe)
• Strong foundation in linear algebra, probability, and optimization

Nice-to-Have

• Experience with 3D vision, depth estimation, or point cloud processing
• Familiarity with generative models (GANs, diffusion models) for image synthesis
• Background in video analysis (tracking, action recognition, optical flow)
• Experience with edge deployment (NVIDIA Jetson, mobile, or browser inference)
• Publications or contributions to computer vision research

Tech Stack

What We Offer

• Competitive salary and equity package
• Flexible remote or hybrid work arrangement
• Health, dental, and vision insurance
• Annual learning and development budget
• Generous PTO policy

Interview Process

1. 1Recruiter phone screen (30 min)
2. 2Technical screen: ML fundamentals and computer vision concepts (45 min)
3. 3Take-home project: train and evaluate a model on a provided dataset (4-5 hours)
4. 4Deep dive on take-home project and system design for a vision pipeline (60 min)
5. 5Team culture and collaboration interview (30 min)